Surveys were used to test for associations between energy density, energy intake and weight status, controlling for age, sex, height, activity level, smoking status, urban residence and income. Pearson correlation coef®cients were calculated to identify nutrient intake correlates of energy density. Replacing energy density with its nutrient correlates (3 day mean estimates of fat, protein, ®ber and water intakes) in the models predicting energy intake and overweight status, the independent effects of speci®c nutrients were investigated. Results: Energy density was positively and signi®cantly associated with energy intake and overweight status. Energy density was signi®cantly correlated with every nutrient examined, although the correlations were weak for most variables except water intake. Only water intake behaved consistently across analyses. It was negatively correlated with energy density, negatively, signi®cantly and independently associated with energy intake as well as overweight status. Despite positive associations with energy intake, fat and protein intake were not signi®cant predictors of overweight status. Fiber intake was strongly and positively associated with overweight status. Conclusions: Of the nutrients examined, only water intake appeared to explain the effects of energy density on energy intake and overweight status.

Data from the 1994 USDA nationwide survey (CSFII) on 190 non-smoking males (aged 20-29) were used to propose a method for adjusting total water intake for the diuretic effects of caffeine and alcohol, and evaluate the potential for related misclassification bias. The data were processed on a per meal basis. Under the assumption that subjects were in water balance at the start of the survey day, water losses due to caffeine (1.17 ml/mg caffeine) and alcohol (10 ml/g alcohol) were subtracted from crude intake estimates. If water intake for one meal was inadequate for excretion of the associated osmotic load at 750 mosmol/l, water losses for the subsequent meal were reduced by 32%. Unadjusted and adjusted mean total water intakes differed by 321.5 g. Misclassification appeared worst at higher water intakes. Linear regression models, each with a water intake variable as an independent variable and body mass index as the outcome, were fit to evaluate the potential for alcohol- and caffeine-related misclassification bias. Misclassification resulted in large changes (all >10%) in linear regression estimates of effect. Future studies of water-disease relationships, especially those intending to compare extremes of total water intake, should consider caffeine- and alcohol-related misclassification bias.